Input method software is a kind of frequently used word inputting system.
A traditional text inputting method usually uses a universal language model as a core to construct the input method. The universal language model is obtained through analyzing a large amount of training material. The training material is usually obtained automatically from the Internet. It represents general an input requirement of most users, i.e., the universal language model is created based on common word-selection habits of most users. However, when inputting words using the input method software, a user usually wants to obtain characters that he constantly uses rapidly. When selecting a word, different users may desire different kinds of candidate sentences ranking in the first according to their different identities, interests and habits, and field of characters that they used to use. For example, a scientific researcher and a bank clerk usually hope that professional terms in their fields are ranked in the first when inputting words. For another example, people from north-east and Sichuan province also hope their own dialect words ranking in the first in the candidate sentence list. The traditional input method which uses the universal language model cannot meet the input requirement of different users. Thus, the input accuracy ratio is low and the input speed of the user is affected.
In addition, a standard Ngram language model modeling method has a defect. On the one hand, the standard Ngram language model is a simplex model. However, in a practical application, the user's Chinese input, handwritten recognizing, voice recognizing requirement are various and unlimited. For example, users may have variable and unlimited requirements such as Chinese input, handwriting recognizing and voice recognizing. For example, a user may write a technical report sometimes and chat on the Internet at other times. Under these two situations, Chinese input requirements of the user are different. For another example, users in different ages may have different speaking habits due to their different living experiences. Reflected on the Chinese input, contents constantly inputted by these users have a big difference. Therefore, a simplex model cannot meet different requirements for inputting Chinese of users of different ages and requirements of one user under different situations. For the different requirements, if the same model is adopted, accuracy of recognizing the input of the user is affected. On the other hand, the standard Ngram language model does not have a self-study mechanism. Once parameters in the standard Ngram language model are determined, the parameters cannot be adjusted intelligently according to inputting habits of the user. Thus, accuracy ratio for recognizing the input of the user is relatively low.